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Investigation:

CD-36 (Bastidas / Shuttleworth / Nobre)

LBA Dataset ID:

CD36_SALDAS

Originator(s):

1. DE GONCALVES, L.G.G.
2. SHUTTLEWORTH, W.J.
3. VILA, D.
4. LARROZA, E.
5. BOTTINO, M.J.
6. HERDIES, D.L.
      7. ARAVEQUIA, J.A.
8. DE MATTOS, J.G.
9. TOLL, D.L.
10. RODELL, M.
11. HOUSER, P.

Point(s) of Contact:

ORNL DAAC User Services Office Oak Ridge National Laboratory Oak Ridge, Tennessee 37 (ornldaac@ornl.gov)

Dataset Abstract:

This product is a result from a joint collaboration between University of Arizona, Hydrological Sciences Branch at NASA - Goddard Space Flight Center and INPE/CPTEC - Instituto Nacional de Pesquisas Espaciais / Centro de Previsao de Tempo e Estudos Climaticos - through the LBA-ECO CD-36 research group. The SALDAS forcing data are atmospheric fields necessary for land surface modeling for South America which are derived by combining modeled and observation based sources. The forcing data cover the entire continent of South America and are built around the model-calculated values of air temperature, wind speed and specific humidity at 2m, surface pressure, downward shortwave and longwave surface radiation, and precipitation from South American Regional Reanalysis (SARR). These SARR data (Aravequia et al. 2007), which were released in 2006 by INPE/CPTEC, are a medium-term, dynamically consistent, high-resolution, high-frequency, atmospheric dataset covering South American. Currently they are available for a 5-year period from 2000 to 2004. The SARR data are derived using the modified version of the Eta model (Chou and Herdies, 1996) and the Regional Physical-space Statistical System (RPSAS) data assimilation scheme applied at 40Km horizontal resolution and 38 vertical levels. This system integrates upper air and surface observations from several sources over South America, including vertical soundings from the RACCI/LBA and SALLJEX field campaigns over the Amazon and the low-level jet regions along the Andes, respectively. The quality of the reanalysis is assumed to be superior to the operational Eta model analyses because the model and data assimilation systems remained frozen during the analysis, a larger number of observation were used, and more output fields were saved therefore allowing more comprehensive evaluation. The topography used in the Eta model when calculating the SARR differs substantially from the SALDAS topography which is derived from USGS GTOPO30 global 30 second elevation map (Row, Hastings, and Dunbar, 1995), and adjustments in the air temperature, humidity, surface pressure and downward longwave radiation are required to allow for these differences in altitude. The air temperature and surface pressure at 2 m are adjusted using the standard vertical atmospheric lapse rate, specific humidity is adjusted by assuming a constant relative humidity between the two elevations, and the longwave radiation is corrected based on the ratio between vapor pressure at the two levels and temperature between the two levels applied to the Stefan-Boltzmann equation. For a more detailed description of the elevation correction procedures, see Cosgrove et al. (2003). Since the main goals of SALDAS is to provide more realistic and accurate datasets over South America than already available from existing global reanalyzes, downward shortwave radiation and precipitation are observation based derived from GOES satellite measurements and real time TRMM Multi-satellite Precipitation Analysis (TMPA-RT) retrievals (Huffman et al, 2007), respectively. In order to reduce the bias of satellite rainfall retrievals, TMPA-RT is combined with surface rain gauges (when available), using additive and multiplicative methods (Vila et al, 2008). The datasets are linearly interpolated in space and time to 1/8 degree resolution and 3-hourly frequency, respectively, except the downward shortwave radiation which is adjusted following changes in the zenithal angle, expressed as a function of hour of the day and latitude.

Beginning Date:

2000-01-01

Ending Date:

2004-12-31

Metadata Last Updated on:

2013-06-03

Data Status:

Archived

Access Constraints:

PUBLIC

Data Center URL:

http://daac.ornl.gov/

Distribution Contact(s):

ORNL DAAC User Services Office Oak Ridge National Laboratory Oak Ridge, Tennessee 37 (ornldaac@ornl.gov)

Access Instructions:

PUBLIC

Data Access:

IMPORTANT: The LBA-ECO Project website is no longer being supported. Links to external websites may be inactive. Final data products from the LBA project can be found at the ORNL DAAC. Please follow the fair use guidelines found in the dataset documentation when using or citing LBA data.
Datafile(s):

LBA-ECO CD-36 South American Land Data Assimilation System Atmospheric Forcing Data:  http://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1162

Documentation/Other Supporting Documents:

LBA-ECO CD-36 South American Land Data Assimilation System Atmospheric Forcing Data:  http://daac.ornl.gov/LBA/guides/CD36_SALDAS.html

Citation Information - Other Details:

de Goncalves, L.G.G., W.J. Shuttleworth, D. Vila, E. Larroza, M.J. Bottino, D.L. Herdies, J.A. Aravequia, J.G. de Mattos, D.L. Toll, M. Rodell and P. Houser. 2013. LBA-ECO CD-36 South American Land Data Assimilation System Atmospheric Forcing Data. Data set. Available on-line [http://daac.ornl.gov] from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A. http://dx.doi.org/10.3334/ORNLDAAC/1162

Keywords - Theme:

Parameter Topic Term Source Sensor
CLIMATE ATMOSPHERE ATMOSPHERIC CONDITIONS COMPUTER MODEL MODEL ANALYSIS
LAND COVER LAND SURFACE NEUTRINOS COMPUTER MODEL MODEL ANALYSIS

Uncontrolled Theme Keyword(s):  LAND DATA ASSIMILATION, LAND SURFACE ATMOSPHERIC FORCING, SOUTH AMERICA LAND SURFACE MODELING, SURFACE HYDROLOGY

Keywords - Place (with associated coordinates):

Region
(click to view profile)
Site
(click to view profile)
North South East West
  SOUTH AMERICA 11.93750 -48.81250 -33.93750 -82.93750

Related Publication(s):

de Goncalves, L.G.G., W.J. Shuttleworth, B. Nijssen, E.J. Burke, J.A. Marengo, S.C. Chou, P. Houser, and D.L. Toll. 2006. Evaluation of model-derived and remotely sensed precipitation products for continental South America. Journal of Geophysical Research-Atmospheres 111(D16):D16113, doi:10.1029/2005JD006276.

de Goncalves, L.G.G., W.J. Shuttleworth, D. Vila, E. Larroza, M.J. Bottino, D.L. Herdies, J.A. Aravequia, J.G. de Mattos, D.L. Toll, M. Rodell and P. Houser, (accepted) The South American Land Data Assimilation System (SALDAS) 5-Year Retrospective Atmospheric Forcing Datasets. Journal of Hydrometeorology (accepted).

de Goncalves, L.G.G., W.J. Shuttleworth, E.J. Burke, P. Houser, D.L. Toll, M. Rodell, and K. Arsenault. 2006. Toward a South America Land Data Assimilation System: Aspects of land surface model spin-up using the simplified simple biosphere. Journal of Geophysical Research-Atmospheres 111(D17):110, doi:10.1029/2005JD006297.

de Goncalves, L.G.G., W.J. Shuttleworth, S.C. Chou, Y.K. Xue, P.R. Houser, D.L. Toll, J. Marengo, and M. Rodell. 2006. Impact of different initial soil moisture fields on Eta model weather forecasts for South America. Journal of Geophysical Research-Atmospheres 111(D17):D17102, doi:10.1029/2005JD006309.

Data Characteristics (Entity and Attribute Overview):

Data Characteristics:

The forcing data cover the entire continent of South America and are built around the model-calculated values of air temperature, wind speed and specific humidity at 2m, surface pressure, downward shortwave and longwave surface radiation, and precipitation from South American Regional Reanalysis (SARR). These SARR data (Aravequia et al. 2007), which were released in 2006 by INPE/CPTEC, are a medium-term, dynamically consistent, high-resolution, high-frequency, atmospheric dataset covering South American. Currently they are available for a 5-year period from 2000 to 2004.




Each Grib file contains:

Near surface wind magnitude [m/s]

Rainfall rate [kg/m^2/s]

Snowfall rate [kg/m^2/s]

Near surface air temperature [K]

Near surface specific humidity [kg/kg]

Surface pressure [Pa]

Surface incident shortwave radiation [W/m^2]

Surface incident longwave radiation [W/m^2]







A GraDS control file SALDAS_FORCING.ctl is made available as an example to visualize the datasets.




The datasets are in Grib format, so use


gribmap -i SALDAS_FORCING.ctl


to generate the map file.

Data Application and Derivation:

Forcing data for atmospheric and land surface models

Quality Assessment (Data Quality Attribute Accuracy Report):

Quality Assessment:

The topography used in the Eta model when calculating the SARR differs substantially from the SALDAS topography which is derived from USGS GTOPO30 global 30 second elevation map (Row, Hastings, and Dunbar, 1995), and adjustments in the air temperature, humidity, surface pressure and downward longwave radiation are required to allow for these differences in altitude. The air temperature and surface pressure at 2 m are adjusted using the standard vertical atmospheric lapse rate, specific humidity is adjusted by assuming a constant relative humidity between the two elevations, and the longwave radiation is corrected based on the ratio between vapor pressure at the two levels and temperature between the two levels applied to the Stefan-Boltzmann equation. For a more detailed description of the elevation correction procedures, see Cosgrove et al. (2003).

Process Description:

Data Acquisition Materials and Methods:

The SARR data are derived using the modified version of the Eta model (Chou and Herdies, 1996) and the Regional Physical-space Statistical System (RPSAS) data assimilation scheme applied at 40Km horizontal resolution and 38 vertical levels. This system integrates upper air and surface observations from several sources over South America, including vertical soundings from the RACCI/LBA and SALLJEX field campaigns over the Amazon and the low-level jet regions along the Andes, respectively. The quality of the reanalysis is assumed to be superior to the operational Eta model analyses because the model and data assimilation systems remained frozen during the analysis, a larger number of observation were used, and more output fields were saved therefore allowing more comprehensive evaluation.



Since the main goal of SALDAS is to provide more realistic and accurate datasets over South America than already available from existing global reanalyzes, downward shortwave radiation and precipitation are observation based derived from GOES satellite measurements and real time TRMM Multisatellite Precipitation Analysis (TMPA-RT) retrievals (Huffman et al, 2007), respectively. In order to reduce the bias of satellite rainfall retrievals, TMPA-RT is combined with surface rain gauges (when available), using additive and multiplicative methods (Vila et al, 2008). The datasets are linearly interpolated in space and time to 1/8 degree resolution and 3-hourly frequency, respectively, except the downward shortwave radiation which is adjusted following changes in the zenithal angle, expressed as a function of hour of the day and latitude.

References:

Aravequia, J. A.; Herdies, D. L.; Sapucci, L. F.; Andreoli, R. V.; Ferreira,

S. F.S.; Goncalves, L.G.G. (2007), Reanalise Regional 2000-2004 sobre a America do Sul com o Modelo RPSAS/ETA: Description do Experimento e dos Produtos Derivados. Boletim da SBMET. V.31 n.02.




Chou, S. C.; Herdies, D. L., 1996: Test runs using Eta model over South America. In: 15th Conference on weather Analysis and Forecasting, 1996, Virginia. 15th conference on weather analysis and forecasting, 1996. v. 1.

Row, L.W., Hastings, D.A., and Dunbar, P.K., 1995. TerrainBase Worldwide Digital Terrain Data - Documentation Manual, CD-ROM Release 1.0. National Geophysical Data Center, Boulder, Colorado.




Cosgrove, B.A., Dag Lohmann, Kenneth E. Mitchell, Paul R. Houser, Eric F. Wood, John C. Schaake, Alan Robock, Curtis Marshall, Justin Sheffield, Qingyun Duan, Lifeng Luo, R. Wayne Higgins, Rachel T. Pinker, J. Dan Tarpley, and Jesse Meng, (2003), Real-time and retrospective forcing in the North American Land Data Assimilation System (NLDAS) project. Journal of Geophysical Research, Vol. 108, No. D22, 8842, doi:10.1029/2002JD003118




Huffman G. J., R. F. Adler, D. T. Bolvin, G. Gu, E. J. Nelkin, K. P. Bowman, Y. Hong, E. F. Stocker and D. B. Wolff (2007), The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine Scales, J. Hydrometeor.,8, 38-55.




Row, Hastings, and Dunbar (1995). [Please provide full citation.]






Vila, D., L.G.G. de Goncalves, J.R. Rozante and D.L. Toll, (in press). Statistical Evaluation of Combined Daily Gauge Observations and Rainfall Satellite Estimations over Continental South America. Journal of Hydrometeorology.

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